Assessment of plant nitrogen stress in wheat (<i>Triticum aestivum</i> L.) through hyperspectral indices

Ranjan, Rajeev; Chopra, Usha Kiran; Sahoo, R. N.; Singh, Anil Kumar; Pradhan, Sanatan

doi:10.1080/01431161.2012.687473

Cited by 67 publications

(31 citation statements)

References 43 publications

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“…Based on the assumption of the consistent relationship between the N accumulation and the canopy reflectance and ratio index across the whole growth period, the VIs (listed in Table 3) were collected from the previous literature [43,47,[52][53][54][55][56][57][58][59][60][61][62][63][64][65][66][67][68][69]. Statistical analyses were performed using SPSS 13.0.…”

Section: Verificaiton Of Model and New Indexmentioning

confidence: 99%

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

et al. 2018

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Abstract:The rapid and accurate estimation of wheat production at a regional scale is crucial for national food security and sustainable agricultural development. This study developed a new gross primary productivity (GPP) estimation model (denoted as the [ACPM]), based on the effects of light, heat, soil moisture, and nitrogen content (N) on the light-use efficiency of winter wheat. The ACPM model used the quantic additivity of the environmental factors to improve the minimum form or multiple multiplication form in the previous model and thus characterized the joint effects of heat, soil moisture, and N on crop photosynthesis performance. The key parameters (i.e., light) were determined from the photosynthetically active radiation product of the Himawari-8 sensor and the fraction of photosynthetically active radiation product of Moderate Resolution Imaging Spectroradiometer (MODIS). The heat was determined from the land temperature products of MODIS. The soil moisture was obtained from the inversion using a visible and shortwave infrared drought index (VSDI), whereas the N stress of winter wheat was detected using the newly developed modified ratio vegetation index (MRVI), which could accurately obtain the spatiotemporal distribution of the leaf chlorophyll content of winter wheat. The ACPM and two other previous models (named the GPP1 and GPP2 models) were applied on the Himawari-8 and MODIS images in Hengshui City. The evaluation results, based on the ground measurement, indicated that the ACPM models exhibited the best estimate of dry aboveground biomass (DAM) and the wheat yield in Hengshui City, with errors of <10% and <12% for the DAM and yield, respectively. Considering the easy operation of the ACPM model and the accessibility of the corresponding satellite images, the Agriculture Crop Photosynthesis Model (ACPM) can be expected to provide information on the winter wheat shortfalls and surplus ahead of the availability of official statistical data.

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Section: Verificaiton Of Model and New Indexmentioning

confidence: 99%

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

et al. 2018

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“…Spectral reflectance has been widely used to detect plant stresses due to its potential of identifying different plant conditions [ 26 , 27 ]. For example, Ranjan et al assessed the effects of plant nitrogen stress on wheat through hyperspectral indices, and found that the spectral reflectance of leaves with different nitrogen content varied greatly in the 400–700 nm range [ 28 ]; Zhang et al investigated plant (wheat, cabbage, locust and ash) responses under heavy metal (Cu) stress, and showed that the reflectance in the 530–570 nm and 800–900 nm regions displayed significant changes [ 29 ]; Delalieux et al found that biotic stress ( Venturia inaequalis exposure) made the spectral reflectance of apple trees show significant changes in the regions of 580–660 nm and 685–715 nm [ 30 ], and Di Vittorio and Biging found that the reflectance of pine needles damaged by ozone (O 3 ) significantly changed in visible region, particularly for the 535–635 nm and 670–685 nm ranges [ 31 ].…”

Section: Introductionmentioning

confidence: 99%

Detecting Sulfuric and Nitric Acid Rain Stresses on Quercus glauca through Hyperspectral Responses

Wang

Zhang

et al. 2018

Sensors

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Acid rain, which has become one of the most severe global environmental issues, is detrimental to plant growth. However, effective methods for monitoring plant responses to acid rain stress are currently lacking. The hyperspectral technique provides a cost-effective and nondestructive way to diagnose acid rain stresses. Taking a widely distributed species (Quercus glauca) in Southern China as an example, this study aims to monitor the hyperspectral responses of Q. glauca to simulated sulfuric acid rain (SAR) and nitric acid rain (NAR). A total of 15 periods of leaf hyperspectral data under four pH levels of SAR and NAR were obtained during the experiment. The results showed that hyperspectral information could be used to distinguish plant responses under acid rain stress. An index (green peak area index, GPAI) was proposed to indicate acid rain stresses, based on the significantly variations in the region of 500–660 nm. Light acid rain (pH 4.5 SAR and NAR) promoted Q. glauca growth relative to the control groups (pH 5.6 SAR and NAR); moderate acid rain (pH 3.0 SAR) firstly promoted and then inhibited plant growth, while pH 3.0 NAR showed mild inhibitory effects during the experiment; and heavy acid rain (pH 2.0) significantly inhibited plant growth. Compared with NAR, SAR induced more serious damages to Q. glauca. These results could help monitor acid rain stress on plants on a regional scale using remote sensing techniques.

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“…In contrast, HSI with remote sensing examines many contiguous, narrow spectral channels (Campbell, 1996); thus, it is able to provide additional information, and no critical data are lost. HSI with remote sensing is better than MSI and demonstrates versatility for a variety of crop monitoring applications (Hunt et al, 1989;Blackburn, 1998;Champagne et al, 2003;Zhu et al, 2006;Wang et al, 2008;Prabhakar et al, 2011;Ranjan et al, 2012;Pradhan et al, 2014;Mahajan et al, 2014;Prasannakumar et al, 2014). HSI has already proven to be very effective in defense, agricultural research, and environmental applications (Lu and Chen, 1999;Ustin et al, 2004;Farley et al, 2007).…”

Section: Hsi In Agriculturementioning

confidence: 99%

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

Ahmed¹,

Yasmin²,

Mo³

et al. 2016

Journal of Biosystems Engineering

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Background: Although hyperspectral imaging was originally introduced for military, remote sensing, and astrophysics applications, the use of analytical hyperspectral imaging techniques has been expanded to include monitoring of agricultural crops and commodities due to the broad range and highly specific and sensitive spectral information that can be acquired. Combining hyperspectral imaging with remote sensing expands the range of targets that can be analyzed. Results: Hyperspectral imaging technology can rapidly provide data suitable for monitoring a wide range of plant conditions such as plant stress, nitrogen status, infections, maturity index, and weed discrimination very rapidly, and its use in remote sensing allows for fast spatial coverage. Conclusions: This paper reviews current research on and potential applications of hyperspectral imaging and remote sensing for outdoor field monitoring of agricultural crops. The instrumentation and the fundamental concepts and approaches of hyperspectral imaging and remote sensing for agriculture are presented, along with more recent developments in agricultural monitoring applications. Also discussed are the challenges and limitations of outdoor applications of hyperspectral imaging technology such as illumination conditions and variations due to leaf and plant orientation.

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Assessment of plant nitrogen stress in wheat (Triticum aestivum L.) through hyperspectral indices

Cited by 67 publications

References 43 publications

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

Winter Wheat Production Estimation Based on Environmental Stress Factors from Satellite Observations

Detecting Sulfuric and Nitric Acid Rain Stresses on Quercus glauca through Hyperspectral Responses

Outdoor Applications of Hyperspectral Imaging Technology for Monitoring Agricultural Crops: A Review

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